Foundations of deep reinforcement learning: theory and practice in Python

by Graesser, Laura

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Title Statements

Full title :Foundations of deep reinforcement learning : theory and practice in Python / Laura Graesser, Wah Loon Keng.

Summary

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games--such as Go, Atari games, and DotA 2--to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.

Edition Statements

Publication Date:©2020

Publication Statements

Publisher(s):Addison-Wesley
Place of Publication:Boston
Language:eng
Publication Date:©2020

The Physical Object

Description:xxiv, 379 pages : : color illustrations ; : 23 cm

Edition Identifiers

ISBN:9780135172384, 0135172381
oclc:on1048968772
LC Classification:Q325.6
Dewey Decimal:006.31

1 Introduction to Reinforcement Learning -- 1.1 Reinforcement Learning -- 1.2 Reinforcement Learning as MDP -- 1.3 Learnable Functions in Reinforcement Learning -- 1.4 Deep Reinforcement Learning Algorithms -- 1.5 Deep Learning for Reinforcement Learning -- 1.6 Reinforcement Learning and Supervised Learning -- 1.6.1 Lack of an Oracle -- 1.6.2 Sparsity of Feedback -- 1.6.3 Data Generation -- I Policy-Based and Value-Based Algorithms --2 Reinforce -- 3 Sarsa -- 4 Deep Q-Networks (DQN) -- 5 Improving DQN -- II Combined Methods -- 6 Advantage Actor-Critic (A2C) -- 7 Proximal Policy Optimization (PPO) -- 8 Parallelization Methods -- 9 Algorithm Summary -- III Practical Details -- 10 Getting Deep RL to Work -- 11 SUM Lab -- 12 Network Architectures --13 Hardware -- IV Environment Design -- 14 States -- 15 Actions -- 16 Rewards -- 17 Transition Function -- A Deep Reinforcement Learning Timeline -- B Example Environments -- B.2 Continuous Environments

Subjects

Apprentissage automatique Apprentissage par renforcement (Intelli… artificial intelligence Intelligence artificielle Machine learning Neural networks (Computer science) Python (Computer program language) Python (Langage de programmation) Reinforcement learning Réseaux neuronaux (Informatique)